aiqbench analysis
This paper proposes a research agenda for data visualization techniques designed to foster healthy skepticism alongside trust, addressing the growing risk of human over-reliance on AI models. By shifting focus from purely explanatory visuals to tools that encourage critical engagement, it tackles a fundamental behavioral challenge in deploying artificial intelligence systems. The work is central to understanding how users interact with and evaluate AI outputs, making it essential reading for researchers in AI safety and human-computer interaction.
Healthy skepticism in AI: a data visualization research agenda
arXiv:2610.09740v1 Announce Type: cross Abstract: Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI. The trustworthiness line of work was built at least partially on an assumption that humans were critical users unlikely to adopt AI technology. It is increasingly clear that human trust levels in AI span, in fact, a wide range from critical to over-reliant. There is an urgent need to support both trust and healthy skepticism in AI solutions. We argue that it is healthy for humans to adopt a skeptical view both on the results of AI models and on the use of such AI models. We share our thoughts on the rising phenomenon of over-reliance on AI models, the risks and opportunities in using AI models, and the role of data visualization in over-reliance situations where humans are not motivated to engage in critical thinking.
Abstract:Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI. The trustworthiness line of work was built at least partially on an assumption that humans were critical users unlikely to adopt AI technology. It is increasingly clear that human trust levels in AI span, in fact, a wide range from critical to over-reliant. There is an urgent need to support both trust and healthy skepticism in AI solutions. We argue that it is healthy for humans to adopt a skeptical view both on the results of AI models and on the use of such AI models. We share our thoughts on the rising phenomenon of over-reliance on AI models, the risks and opportunities in using AI models, and the role of data visualization in over-reliance situations where humans are not motivated to engage in critical thinking.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2610.09740 [cs.HC]
(or arXiv:2610.09740v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2610.09740 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 243 words
